Agentic AI
AI systems that autonomously plan and execute multi-step tasks, using tools and taking actions to achieve goals without step-by-step human instruction.
Agentic AI systems go beyond single-turn question answering. An agentic document intelligence system might receive a high-level goal — "review all contracts in this folder and identify those with auto-renewal provisions expiring in the next 90 days" — and autonomously decompose that into subtasks: list all contracts, extract renewal provisions from each, identify expiration dates, filter by the 90-day window, and produce a summary report. Each step involves tool use (document retrieval, data extraction, date calculation) that the agent orchestrates without human direction.
The reliability and trustworthiness of agentic systems depend heavily on the accuracy of each intermediate step, since errors compound across a multi-step workflow. In legal and compliance contexts, agentic workflows must include human review checkpoints for high-stakes outputs. Citation verification at every step — so humans can inspect the agent's reasoning — is particularly important when agents take actions based on document analysis.
Related Terms
More ai/ml Terms
Retrieval-Augmented Generation (RAG)
An AI architecture that combines information retrieval with text generation to produce answers grounded in source documents.
Vector Embedding
A numerical representation of text as a high-dimensional vector, enabling semantic similarity comparisons between passages.
BM25
A probabilistic keyword-ranking algorithm that scores documents by term frequency and inverse document frequency.
Chunking
The process of splitting large documents into smaller, overlapping segments optimized for retrieval and embedding.
Hallucination
When an AI model generates plausible-sounding but factually incorrect or fabricated information.
Large Language Model (LLM)
A neural network trained on massive text corpora that can understand and generate human language.
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